State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng; Guangzhao Luo; Fei Gao

doi:10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng
, Guangzhao Luo
, Fei Gao

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

Original language	English
Title of host publication	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479942398
DOIs	https://doi.org/10.1109/ITEC-AP.2014.6940680
State	Published - 30 Oct 2014
Event	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Beijing, China Duration: 31 Aug 2014 → 3 Sep 2014

Publication series

Name	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

Conference

Conference	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014
Country/Territory	China
City	Beijing
Period	31/08/14 → 3/09/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

adaptive unscented Kalman filter (AUKF)
Battery
least squares support vector machine (LSSVM)
state of charge (SOC)

Access to Document

10.1109/ITEC-AP.2014.6940680

Cite this

Meng, J., Luo, G., & Gao, F. (2014). State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. In IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings Article 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITEC-AP.2014.6940680

Meng, Jinhao ; Luo, Guangzhao ; Gao, Fei. / State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

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title = "State-of-charge estimation for lithium-ion battery using AUKF and LSSVM",

abstract = "A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.",

keywords = "adaptive unscented Kalman filter (AUKF), Battery, least squares support vector machine (LSSVM), state of charge (SOC)",

author = "Jinhao Meng and Guangzhao Luo and Fei Gao",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 ; Conference date: 31-08-2014 Through 03-09-2014",

year = "2014",

month = oct,

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doi = "10.1109/ITEC-AP.2014.6940680",

language = "英语",

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Meng, J, Luo, G & Gao, F 2014, State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. in IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings., 6940680, IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014, Beijing, China, 31/08/14. https://doi.org/10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. / Meng, Jinhao; Luo, Guangzhao; Gao, Fei.
IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

AU - Meng, Jinhao

AU - Luo, Guangzhao

AU - Gao, Fei

PY - 2014/10/30

Y1 - 2014/10/30

N2 - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

AB - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

KW - adaptive unscented Kalman filter (AUKF)

KW - Battery

KW - least squares support vector machine (LSSVM)

KW - state of charge (SOC)

UR - https://www.scopus.com/pages/publications/84916235076

U2 - 10.1109/ITEC-AP.2014.6940680

DO - 10.1109/ITEC-AP.2014.6940680

M3 - 会议稿件

AN - SCOPUS:84916235076

T3 - IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

BT - IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014

Y2 - 31 August 2014 through 3 September 2014

ER -

Meng J, Luo G, Gao F. State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. In IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. 6940680. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). doi: 10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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